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Text Categorization

Text Categorization is the task of automatically assigning pre-defined categories to documents written in natural languages. Several types of Text Categorization have been studied, each of which deals with different types of documents and categories, such as topic categorization to detect discussed topics (e.g., sports, politics), spam detection, and sentiment classification to determine the sentiment typically in product or movie reviews.

Source: Effective Use of Word Order for Text Categorization with Convolutional Neural Networks

Papers

Showing 8190 of 247 papers

TitleStatusHype
Modeling Trolling in Social Media Conversations0
Linking, Searching, and Visualizing Entities in Wikipedia0
Multilingual Multi-class Sentiment Classification Using Convolutional Neural NetworksCode0
Online Multi-Label Classification: A Label Compression Method0
Annotation Artifacts in Natural Language Inference Data0
Authorship Attribution Using the Chaos Game RepresentationCode0
Automatic Generation of Language-Independent Features for Cross-Lingual Classification0
Deep Learning of Nonnegativity-Constrained Autoencoders for Enhanced Understanding of Data0
Textual Relations and Topic-Projection: Issues in Text Categorization0
Compression-Based Regularization with an Application to Multi-Task Learning0
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